مروری سیستماتیک بر تحلیل جهات توسعه شهری با استفاده از الگوریتم یادگیری ماشین

نوع مقاله : مروری

نویسندگان

گروه شهرسازی،دانشکده هنر و معماری، دانشگاه یزد، یزد ، ایران

10.22098/gsd.2025.16837.1081

چکیده

توسعه شهری یکی از پویاترین فرایندهای اجتماعی، اقتصادی و زیست‌محیطی است که تأثیرات گسترده‌ای بر جوامع انسانی و محیط‌زیست دارد. بررسی و تحلیل جهات توسعه شهری، به‌ویژه در مناطقی که با رشد سریع شهری و تغییرات جمعیتی مواجه هستند، برای مدیریت کارآمد منابع و برنامه‌ریزی پایدار اهمیت بسیاری دارد. هدف این پژوهش، بررسی نقش الگوریتم‌های یادگیری ماشین در تحلیل و پیش‌بینی روندهای توسعه شهری با تأکید بر ترکیب آن‌ها با سیستم‌های اطلاعات جغرافیایی (GIS) و تصاویر ماهواره‌ای است. با جستجو در پایگاه‌های اطلاعاتی مانند Google Scholar و Scopus در بازه زمانی ۲۰۰۶ تا ۲۰۲۴، به نتایج متنوعی دست یافتیم. از میان مقالات بررسی‌شده، ۸۳ مقاله انتخاب و تحلیل شدند. برای تجزیه‌وتحلیل داده‌ها از نرم‌افزار MAXQDA استفاده شد که امکان شناسایی و طبقه‌بندی مفاهیم کلیدی را فراهم کرد. مقالات بر اساس معیارهایی همچون اعتبار مجلات، روش‌شناسی پژوهش و ارتباط با موضوع انتخاب شدند. داده‌ها با استفاده از روش تحلیل محتوای کیفی در نرم‌افزار MAXQDA کدگذاری و دسته‌بندی شدند. نتایج نشان داد که استفاده از ترکیب الگوریتم‌های یادگیری ماشین و GIS، دقت پیش‌بینی توسعه شهری را بین 10٪ تا 40٪ نسبت به مدل‌های سنتی بهبود بخشید است. همچنین، در بین روش‌های موردبررسی، مدل جنگل تصادفی عملکرد بهتری نسبت به ماشین بردار پشتیبان و شبکه عصبی مصنوعی داشته است. این مطالعه با ارائه یک مرور سیستماتیک و تحلیل مقایسه‌ای الگوریتم‌های یادگیری ماشین، نشان می‌دهد که ترکیب این روش‌ها با داده‌های مکانی و تصاویر ماهواره‌ای می‌تواند دقت مدل‌سازی توسعه شهری را بهبود بخشد..  

کلیدواژه‌ها

عنوان مقاله [English]

A Systematic Review of Urban Development Direction Analysis Using Machine Learning Algorithms

نویسندگان [English]

  • Khalilurahman Haidari
  • MOHSEN RAFIEIAN
  • Mohammadreza Noghsanmohammadi
Department of Urban Planning, Faculty of Art and Architecture, University of Yazd, Yazd, Iran
چکیده [English]

Extended Abstract
Introduction
In recent decades, the rapid trends of urbanization and urban development have posed significant challenges for urban planning and management. These challenges include the management of resources and infrastructure, the quality of life of citizens, and the preservation of the environment. Given the accelerating growth of cities, identifying the patterns and key factors influencing urban development is essential for predicting and improving future trends. The application of machine learning algorithms—recognized as a branch of artificial intelligence—has created new opportunities in the analysis and modeling of urban development.
Urban development is influenced by various economic, social, and environmental factors, and accurately predicting it is of great importance to policymakers and urban planners. Despite technological advancements and the increasing availability of big data, analyzing and modeling these trends remain challenging. Integrating multidimensional data—including spatial, social, and economic data—and analyzing them through machine learning methods can provide a deeper understanding of urban development.
The primary objective of this study is to evaluate the effectiveness of various machine learning algorithms in analyzing urban development directions and identifying the associated challenges and opportunities. Specifically, this research aims to:
Assess the accuracy and performance of different algorithms in predicting urban development.
Analyze the integration of machine learning algorithms with Geographic Information Systems (GIS) and satellite imagery.
Investigate the challenges and limitations of using these methods in urban studies.
 
 
Methodology
In this study, MAXQDA software was used to conduct a systematic content analysis of research articles related to the analysis of urban development directions using machine learning algorithms. After preparing and importing the selected articles into the software, the coding process was carried out in three stages: open coding, axial coding, and selective coding. Using MAXQDA’s qualitative and quantitative analysis tools—such as Word Frequency, Code Matrix Browser, and Comparison Diagram—key concepts, relationships between codes, and methodological differences were examined.
To ensure the validity and reliability of the analysis, peer review and Cohen’s Kappa coefficient were employed. Finally, the findings were organized into themes and patterns, and compared with the existing literature to develop a conceptual framework for analyzing urban development.
 
Results and discussion
The results of this study indicate that algorithms such as Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) play a significant role in predicting urban development. Integrating these algorithms with tools such as Geographic Information Systems (GIS) and satellite image analysis has enhanced modeling accuracy. However, challenges remain, including data quality issues, appropriate parameter selection, and the computational complexity of these models.
 
Conclusion
This study highlights that machine learning can serve as a powerful tool for forecasting and analyzing urban development trends. Nevertheless, improving model accuracy and reducing prediction errors require the use of hybrid approaches and higher-quality data. The findings of this research can assist urban policymakers in formulating optimal strategies for sustainable development.
 
Funding
There is no funding support.
 
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
 We are grateful to all the scientific consultants of this paper.
 
 A B S T R A C T
Urban development is one of the most dynamic socio-economic and environmental processes, with far-reaching impacts on human societies and the environment. Analyzing the directions of urban expansion—especially in areas experiencing rapid urban growth and demographic changes—is crucial for efficient resource management and sustainable planning. This study aims to examine the role of machine learning algorithms in analyzing and predicting urban development trends, with a particular focus on their integration with Geographic Information Systems (GIS) and satellite imagery. A systematic search was conducted in databases such as Google Scholar and Scopus, covering the period from 2006 to 2024. From the reviewed literature, 83 articles were selected and analyzed. MAXQDA software was used for data analysis, enabling the identification and classification of key concepts. The selection of articles was based on criteria such as journal credibility, research methodology, and relevance to the topic. The data were coded and categorized using qualitative content analysis in MAXQDA. The results indicated that integrating machine learning algorithms with GIS improved the accuracy of urban development prediction by 10% to 40% compared to traditional models. Among the studied methods, the Random Forest model outperformed Support Vector Machines (SVM) and Artificial Neural Networks (ANN). This study, through a systematic review and comparative analysis of machine learning algorithms, demonstrates that combining these methods with spatial data and satellite imagery can significantly enhance the accuracy of urban development modeling.

کلیدواژه‌ها [English]

  • Systematic Review
  • Urban Development
  • Machine Learning Algorithms
  • Development Approach
  • Spatial Data

مراجع

  1. ترابی، محمدامین؛ رحمانی، فائزه؛ قبادی لموکی، تحفه؛ حاجی بابایی، حسین و فانی، مجید. (1400). پیش بینی و کنترل هوشمند چراغ های راهنمایی با استفاده از الگوریتم هوش مصنوعی خودکار یادگیر. مطالعات مدیریت ترافیک، (61 )، 1-36.
  2. رحمانی، محمد. (1391). کلان‌شهر ویژگی‌ها و مفاهیم. تهران: انتشارات امید انقلاب.
  3. سهرابی، محمدرضا. (1392). اصول نگارش مقالات مروری. مجله پژوهشی دانشگاه علوم پزشکی شهید بهشتی، 18 (2)، 56-52.
  4. شماعی، علی؛ واحدی نژاد، سیدحسین؛ آذرشب، صغری و قاسمی، مسلم. (1401). تحلیل فرایند توسعه کالبدی شهر و ارائه الگوی راهبردی مناسب آن، مطالعه موردی: شهر اهواز. تحقیقات کاربردی علوم جغرافیایی، 22 (67)، 39-25.
  5. شهسواری، امیر و علم الهدی، جمیله. (1398). روش‌شناسی پژوهش مروری و نقش آن در تولید دانش: توسعه یک گونه‌شناسی. روش شناسی علوم انسانی، 25(98)، 79-105. doi: 10.30471/mssh.2019.1572
  6. کریمی ناصری، پرویز. (1375). شهرها، فقر و توسعه. تهران: اداره کل روابط عمومی و بین‌الملل شهرداری تهران.
  7. محمدی، حمید و قربی، میترا. (1351). مفاهیم کلیدی در مطالعات شهری و منطقه‌ای. یزد: انتشارات دانشگاه یزد.
  8. ملبوس باف، رامین و عزیزی، فریدون. (1389). مرور سیستماتیک “Systematic Review” چیست و چگونه نگاشته می شود؟ (مقاله مروری). پژوهش در پزشکی، 34(3)، 203-207.
  9. Akdeniz, H., Sag, N., & Inam, S. (2022). Analysis of land use/land cover changes and prediction of future changes with land change modeler: Case of Belek, Turkey. Environ Monit Assess 195, 135. https://doi.org/10.1007/s10661-022-10746-w
  10. Aryal, J., Sitaula, C., & Frery, A. (2023). Land use and land cover (LULC) performance modeling using machine learning algorithms: A case study of the city of Melbourne. Sci Rep 13, 13510. https://doi.org/10.1038/s41598-023-40564-0
  11. Ashwini, K., Ashwini, K., Briti Sundar, S., Abdulla Al, K., Altuwaijri, H., Nath, H., & Rahaman, Z. (2024). Harnessing machine learning algorithms to model the association between land use/land cover change and heatwave dynamics for enhanced environmental management. Land, 13(8), 1273. https://doi.org/10.3390/land13081273
  12. Basheer, S., Wang, X., Farooque, A., Nawaz, R., Liu, K., Adekanmbi, T., & Liu, S. (2022). Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques. Remote Sensing, 14(19), 4978. https://doi.org/10.3390/rs14194978
  13. Bindajam, A., Mallick, J., Talukdar, S., Towfiqul Islam, A., & Alqadhi, S. (2021). Integration of artificial intelligence–based LULC mapping and prediction for estimating ecosystem services for urban sustainability: Past to future perspective. Arabian Journal of Geosciences, 14, 1887. https://doi.org/10.1007/s12517-021-08251-4
  14. Chang, N.-B., Han, M., Yao, W., Chen, L.-C., & Xu, S. (2010). Change detection of land use and land cover in an urban region with SPOT-5 images and partial Lanczos extreme learning machine. Remote Sensing, 4 (1), https://doi.org/10.1117/1.3518096
  15. Cheng, J., & Masser, I. (2003). Urban growth pattern modeling. Landscape and Urban Planning, 62(4), 199-217. https://doi.org/10.1016/S0169-2046(02)00150-0
  16. Cheng, L. L., Tian, C., & Yin, T. T. (2022). Identifying driving factors of urban land expansion using Google Earth Engine and machine learning approaches in Mentougou District, China. Scientific Reports,12, 16248. https://doi.org/10.1038/s41598-022-20478-z.
  17. Chughtai, A., Abbasi, H., & Karas, I. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment, 22, 100482. https://doi.org/10.1016/j.rsase.2021.100482
  18. Das, S., & Angadi, D. (2020). Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: A micro-level study. GeoJournal, 87, 2101–2123. https://doi.org/10.1007/s10708-020-10359-1
  19. Feizizadeh, B., Mohammadzade, K., Lakes, T., Blaschke, T., & Omarzadeh, D. (2021). A comparison of the integrated fuzzy object-based deep learning approach and three machine learning techniques for land use/cover change monitoring and environmental impacts assessment. GIScience & Remote Sensing, 58(8), 1543–1570. https://doi.org/10.1080/15481603.2021.2000350
  20. Fontana, A., Nascimento, V., Ometto, J., & Fernandes do Amaral, F. (2023). Analysis of past and future urban growth on a regional scale using remote sensing and machine learning. Remote Sensing,4, 1123254. https://doi.org/10.3389/frsen.2023.1123254
  21. Fu, P., & Weng, Q. (2016). A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment, 175, 205-214. https://doi.org/10.1016/j.rse.2015.12.040
  22. Gupta, P., Haryani, S., & Gupta, V. (2024). Analysis of land use and land cover change detection for Indore District of Malwa Plateau Region using supervised machine learning. International Journal of Experimental, 38, 154–163. https://doi.org/10.52756/ijerr.2024.v38.014
  23. Hassan, Z., Shabbir, R., Ahmad, S., Malik, A., Aziz, N., Butt, A., & Erum, S. (2016). Dynamics of land use and land cover change (LULCC) using geospatial techniques: A case study of Islamabad Pakistan. SpringerPlus, 5, 812. https://doi.org/10.1186/s40064-016-2414-z
  24. He, C., Wei, A., Shia, P., Zhang, Q., & Zhao, Y. (2011). Detecting land-use/land-cover change in rural–urban fringe areas using extended change-vector analysis. Applied Earth Observation and Geoinformation, 13(4), 572-585. https://doi.org/10.1016/j.jag.2011.03.002
  25. Hongwei, Z., Bingfang, W., Shuai, W., Walter, M., Fuyou, T., Zama Eric, M., ... Mavengahama, S. (2020). A synthesizing land-cover classification method based on Google Earth Engine: A case study in Nzhelele and Levhuvu Catchments, South Africa. Chinese Geographical Science, 30, 397–409. https://doi.org/10.1007/s11769-020-1119-y
  26. Huang, B., Zhang, L., & Wu, B. (2009). Spatiotemporal analysis of rural urban land conversion. International Journal of Geographical Information Science, 23(3), 379–398. https://doi.org/10.1080/13658810802119685
  27. Illarionova, S., Tregubova, P., Shukhratov, I., Shadrin, D., Efimov, A., & Burnaev, E. (2024). Advancing forest carbon stocks’ mapping using a hierarchical approach with machine learning and satellite imagery. Scientific Reports, 14, 21032.https://doi.org/10.1038/s41598-024-71133-8
  28. Kalkhajeh, R., & Jamali, A. (2019). Analysis and predicting the trend of land use/cover changes using neural network and systematic points statistical analysis (SPSA). Journal of the Indian Society of Remote Sensing, 47, 1471–1485. https://doi.org/10.1007/s12524-019-00995-7
  29. Karimi Naseri, P. (1996). Cities, Poverty, and Development. Tehran: Public Relations and International Affairs Office of Tehran Municipality. [in Persian].
  30. Karimi, F., Sultana, S., Babakan, A., & Suthaharan, S. (2019). An enhanced support vector machine model for urban expansion prediction. Environment and Urban Systems, 75, 61-75. https://doi.org/10.1016/j.compenvurbsys.2019.01.001
  31. Li, D., Deng, L., & Cai, Z. (2020). Intelligent vehicle network system and smart city management based on genetic algorithms and image perception. Mechanical Systems and Signal Processing, 141, 106623. https://doi.org/10.1016/j.ymssp.2020.106623Get rights and content
  32. Li, Y., Li, M., Li, C., & Liu, Z. (2020). Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Scientific Reports, 10, 9952. https://doi.org/10.1038/s41598-020-67024-3
  33. Loukika, K., Keesara, V., & Sridhar, V. (2021). Analysis of land use and land cover using machine learning algorithms on Google Earth Engine for Munneru River Basin, India. Sustainability.
  34. Lu, Y., Wu, P., Ma, X., & Li, X. (2019). Detection and prediction of land use/land cover change using spatiotemporal data fusion and the Cellular Automata–Markov model. Environmental Monitoring and Assessment,  191, 68. https://doi.org/10.1007/s10661-019-7200-2
  35. Malboos Baf, R., & Azizi, F. (2010). What is a systematic review and how is it written? (Review article). Research in Medicine, 34(3), 203–207. [in Persian].
  36. Marie Gulsrud, N., Raymond, C., Rutt, R., Olafsson, A. S., Plienger, T., Sandberg, M., ... Jonsson, K. (2018). Rage against the machine’? The opportunities and risks concerning the automation of urban green infrastructure. Landscape and Urban Planning, 180, 85-92. https://doi.org/10.1016/j.landurbplan.2018.08.012
  37. Megahed, Y., Cabral, P., Joel, S., & Caetano, M. (2015). Land cover mapping analysis and urban growth modelling using remote sensing techniques in Greater Cairo Region—Egypt. Geo-Information, 4(3), 1750-1769. https://doi.org/10.3390/ijgi4031750
  38. Michael, B. (2013). The new science of cities. MIT Press.
  39. Milojevic, D., & Creutig, F. (2021). Machine learning for geographically differentiated climate change mitigation in urban areas. Mercator Research Institute on Global Commons and Climate Change.
  40. Mohammadi, H., & Ghorbi, M. (1972). Key Concepts in Urban and Regional Studies. Yazd: Yazd University Press. [in Persian].
  41. Mutale, B., Withanage, N., Mishra, P., Shen, J., Abdelrahman, K., & Fnais, M. (2024). A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: A case from Lusaka and Colombo. Frontiers in Environmental Science, 12, 1431645 https://doi.org/10.3389/fenvs.2024.1431645
  42. Nyamekye, C., Kwofie, S., Ghansah, B., Agyapong, E., & Boamah, L. (2020). Assessing urban growth in Ghana using machine learning and intensity analysis: A case study of the New Juaben Municipality. Elsevier.
  43. Ouma, Y., Nkwae, B., Odirile, P., Parida, B., Anderson, G., & Qi, J. (2022). Comparison of machine learning classifiers for multitemporal and multisensor mapping of urban LULC features. Remote Sensing, 14, 5455–5470. https://doi.org/10.5194/bg-14-5455-2017, 2017
  44. Patel, S., Verma, P., & Singh, G. (2019). Agricultural growth and land use land cover change in peri-urban India. Environmental Monitoring and Assessment, 91, 600. https://doi.org/10.1007/s10661-019-7736-1
  45. Rahmani, M. (2012). Metropolis: Characteristics and Concepts. Tehran: Omid Enqelab Publishing. [in Persian].
  46. Rao, P., Tassinari, P., & Torreggiani, D. (2023). Exploring the land-use urban heat island nexus under climate change conditions using machine learning approach: A spatio-temporal analysis of remotely sensed data. Heliyon, 9 (8), e18423. https://doi.org/10.1016/j.heliyon.2023.e18423
  47. Reba, M., & Seto, K. (2020). A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change. Remote Sensing of Environment, 241, 111739. https://doi.org/10.1016/j.rse.2020.111739
  48. Shahsavari, A., & Alamolhoda, J. (2019). Review research methodology and its role in knowledge production: Developing a typology. Humanities Research Methodology, 25(98), 79–105. https://doi.org/10.30471/mssh.2019.1572[in Persian].
  49. Shamaei, A., Vahedi Nejad, S. H., Azarshab, S., & Ghasemi, M. (2022). Analysis of the urban physical development process and presentation of an appropriate strategic model: Case study of Ahvaz city. Applied Research in Geographical Sciences, 22(67), 25–39. [in Persian].
  50. Sohrabi, M. R. (2013). Principles of writing review articles. Research Journal of Shahid Beheshti University of Medical Sciences, 18(2), 52–56. [in Persian].
  51. Srivastava, A. (2017). Estimation of change in LU/LC mapping with classification of digital signature using AI/ML techniques over Google Earth Engine. Thesis submitted in partial fulfillment for the Award of Degree of Doctor of Philosophy.
  52. Tariq, A., & Shu, H. (2020). CA-Markov chain analysis of seasonal land surface temperature and land use land cover change using optical multi-temporal satellite data of Faisalabad, Pakistan. Remote Sensing, 12(20), 3402. https://doi.org/10.3390/rs12203402
  53. Torabi, M. A., Rahmani, F., Ghobadi Lamouki, T., Hajibabaei, H., & Fani, M. (2021). Intelligent prediction and control of traffic lights using self-learning artificial intelligence algorithms. Traffic Management Studies, (61), 1–36. [in Persian].
  54. Ullah, S., Qiao, X., & Abbas, M. (2024). [Title missing]. Scientific Reports.
  55. Ullah, Z., AL-Turjman, F., Mostarda, L., & Gagliardi, R. (2020). Applications of artificial intelligence and machine learning in smart cities. Computer Communications, 154, 313-323. https://doi.org/10.1016/j.comcom.2020.02.069
  56. Verma, P., Raghubanshi, A., Srivastava, P., & Raghubanshi, A. (2020). Appraisal of kappa-based metrics and disagreement indices of accuracy assessment for parametric and nonparametric techniques used in LULC classification and change detection. Modeling Earth Systems and Environment,6, 1045–1059. https://doi.org/10.1007/s40808-020-00740-x
  57. Wang, S., Gebru, B., Lamchin, M., Kayastha, R., & Lee, W.-K. (2020). Land use and land cover change detection and prediction in the Kathmandu District of Nepal using remote sensing and GIS. Sustainability, 12(9), 3925. https://doi.org/10.3390/su12093925
  58. Zhang, C., & Li, X. (2022). Land use and land cover mapping in the era of big data. Land, 11(10), 1692. https://doi.org/10.3390/land11101692
  59. Zhang, H., Qi, Z.-F., Ye, X.-Y., Cai, Y.-B., Ma, W.-C., & Chen, M.-N. (2013). Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China. Applied Geography, 44, 121-133. https://doi.org/10.1016/j.apgeog.2013.07.021
جغرافیا و توسعه فضایی
دوره 1، شماره 4
دی 1403
صفحه 59-75

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